{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import soundfile as sf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def qsnr(orig, quant):\n",
    "    \"\"\"Calculate the QSNR between two tensors\n",
    "    \"\"\"\n",
    "    qerr = orig - quant\n",
    "    sum_err = np.sum(qerr * qerr)\n",
    "    sum_orig = np.sum(orig * orig)\n",
    "    if sum_err > 0:\n",
    "        if sum_orig < sum_err:\n",
    "            if sum_orig == 0:\n",
    "                return -math.inf\n",
    "            else:\n",
    "                # Means error is larger than signal\n",
    "                return -int(round(10 * math.log10(sum_err/sum_orig), 0))\n",
    "        # Error portion of signal\n",
    "        return int(round(10 * math.log10(sum_orig/sum_err), 0))\n",
    "    # Means no error\n",
    "    return math.inf\n",
    "\n",
    "def mse(orig, quant, Size):\n",
    "    mse = 0\n",
    "    for i in range(Size):\n",
    "        mse += (orig.real[i] - quant.real[i]) ** 2\n",
    "        mse += (orig.imag[i] - quant.imag[i]) ** 2\n",
    "    return mse\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, samplerate = sf.read(\"samples/yes.wav\")\n",
    "n_fft = 512\n",
    "int_n_fft = n_fft // 2\n",
    "frame_size = 512\n",
    "frame_step = 160\n",
    "Q_in = 15\n",
    "Q_fft = 15 - (int(np.log2(int_n_fft)) - 1)\n",
    "Q_ifft = Q_fft - (int(np.log2(int_n_fft)) - 1) + int(np.log2(n_fft))\n",
    "\n",
    "frame_idx= 0\n",
    "frame = data[frame_idx*frame_step:frame_idx*frame_step+frame_size]\n",
    "win_frame = frame * np.hamming(frame_size)\n",
    "out_rfft_np = np.fft.rfft(win_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Shift = 4\n",
    "out_preemph_c = np.array([\n",
    "\t\t-1456, -2816, -1776, -1520, -1920, -2416, -2128, -2128, -2096, -1952, -2544, -2368, -2032, -1120, -1616, -2416, -2464, -2640, -1984, -1552, -1488, -1472, -1664, -2480, -2352, -1680, -1616, -1616, -1728, -1760, -1872, -2224, -2016, -2256, -2528, -2000, -2144, -1904, -1504, -1952, -2080, -2336, -2608, -1568, -1824, -2384, -1760, -1648, -1872, -2480, -2848, -2640, -2176, -1984, -2336, -2464, -2592, -2240, -2240, -2576, -2128, -2000, -2064, -1808, -1920, -2736, -2720, -2080, -2064, -1824, -1936, -1984, -1712, -1968, -2032, -1856, -1808, -1760, -1712, -2320, -2000, -1712, -2016, -2064, -2144, -1872, -1936, -1504, -1824, -2672, -2208, -2000, -2176, -1984, -2416, -1936, -1712, -2400, -1648, -1536, -2224, -2672, -2560, -2288, -2640, -1936, -1744, -2448, -2656, -2544, -2032, -2272, -2416, -2160, -1840, -2400, -2912, -1872, -1072, -1456, -3136, -2896, -2064, -2528, -1696, -2272, -3392, -1888, -1024, -2096, -2320, -1888, -1872, -2576, -3520, -2944, -2384, -816, -704, -2272, -1760, -1344, -1648, -832, -1296, -2320, -960, -1840, -2432, -864, -2912, -3904, 800, 880, -3328, -3216, -800, 1376, -2368, -6512, -2144, 1088, -1712, -2272, -544, -2560, -2320, -2064, -4080, -304, 1536, -2480, -5648, -6160, -2576, 1072, 592, -1408, -2128, -2096, -2608, -2000, -1520, -2176, 112, -112, -3152, -2368, -3168, -2960, -464, -400, -880, -1744, -2368, -1888, -2144, -4720, -5584, -1680, 1376, -560, -2096, -352, 160, -1024, -2416, -2624, -1840, -1472, -1744, -2464, -1984, -1712, -2288, -1440, -512, -880, -1504, -2000, -1696, -2080, -2768, -2816, -2928, -1712, -288, -512, -1648, -1056, -288, -576, -1104, -2144, -2208, -1856, -2064, -2320, -1040, 336, -880, -2544, -3440, -2704, -208, -256, -2848, -3712, -1936, -384, -1296, -2032, -1216, -512, -1152, -1984, -1248, -848, -1104, -1776, -1216, -672, -1744, -1104, -512, -1248, -2128, -1728, -784, -1424, -1392, -880, -1360, -1552, -1632, -2080, -1632, -560, -656, -1296, -1584, -1936, -1760, -1680, -1520, -848, -1056, -1680, -1440, -1104, -1760, -2128, -1840, -1344, -752, -1600, -2112, -992, -1472, -2224, -1536, -1184, -1104, -1424, -2064, -1920, -976, -1536, -2336, -1792, -624, -784, -2048, -1552, -1104, -1088, -1360, -2112, -1856, -992, -1552, -2016, -1424, -1760, -1584, -1536, -1376, -1072, -1408, -1744, -1712, -1632, -1760, -1488, -1344, -1472, -2080, -2048, -1792, -1536, -1904, -2160, -2000, -2336, -1568, -1776, -2800, -1888, -816, -1568, -2416, -1808, -1712, -1552, -1408, -2208, -2432, -1872, -1440, -1728, -2272, -2080, -1424, -1504, -2080, -2080, -2096, -1504, -720, -1440, -1664, -1856, -2336, -1520, -1552, -1936, -1904, -1952, -1184, -1760, -2448, -1952, -1584, -1728, -2384, -2064, -992, -1328, -2640, -2096, -992, -688, -688, -1408, -1888, -1568, -1456, -1792, -1904, -1824, -1648, -1696, -1616, -1376, -1824, -1792, -1712, -1744, -1584, -1264, -1664, -1968, -1088, -1728, -1552, -1120, -1648, -1536, -2016, -2160, -1600, -688, -1056, -2272, -1536, -1552, -1504, -832, -1392, -1888, -1984, -1216, -672, -1424, -2000, -1552, -1824, -1952, -1232, -1376, -1648, -1552, -1328, -1360, -2064, -1984, -1696, -2144, -2000, -1664, -1136, -1776, -2048, -1520, -1344, -1072, -1968, -1840, -1248, -912, -912, -1568, -1520, -1376, -1888, -2144, -1296, -1056, -1600, -1904, -1968, -1232, -464, -1056, -2000, -2160, -1440, -1008, -1904, -1264, -512, -1072, -1504, -1920, -1520, -1056, -992, -1168, -1456, -1248, -1392, -1904, -1536, -1248, -1856, -1712, -1168, -1664, -1984, -1664, -1008, -880, -1152, -1264, -1216, -1184, -1616, -1408, -1200, -1344, -1088, -960, ])\n",
    "\n",
    "input_frame = np.array([\n",
    "\t\t-91, -176, -111, -95, -120, -151, -133, -133, -131, -122, -159, -148, -127, -70, -101, -151, -154, -165, -124, -97, -93, -92, -104, -155, -147, -105, -101, -101, -108, -110, -117, -139, -126, -141, -158, -125, -134, -119, -94, -122, -130, -146, -163, -98, -114, -149, -110, -103, -117, -155, -178, -165, -136, -124, -146, -154, -162, -140, -140, -161, -133, -125, -129, -113, -120, -171, -170, -130, -129, -114, -121, -124, -107, -123, -127, -116, -113, -110, -107, -145, -125, -107, -126, -129, -134, -117, -121, -94, -114, -167, -138, -125, -136, -124, -151, -121, -107, -150, -103, -96, -139, -167, -160, -143, -165, -121, -109, -153, -166, -159, -127, -142, -151, -135, -115, -150, -182, -117, -67, -91, -196, -181, -129, -158, -106, -142, -212, -118, -64, -131, -145, -118, -117, -161, -220, -184, -149, -51, -44, -142, -110, -84, -103, -52, -81, -145, -60, -115, -152, -54, -182, -244, 50, 55, -208, -201, -50, 86, -148, -407, -134, 68, -107, -142, -34, -160, -145, -129, -255, -19, 96, -155, -353, -385, -161, 67, 37, -88, -133, -131, -163, -125, -95, -136, 7, -7, -197, -148, -198, -185, -29, -25, -55, -109, -148, -118, -134, -295, -349, -105, 86, -35, -131, -22, 10, -64, -151, -164, -115, -92, -109, -154, -124, -107, -143, -90, -32, -55, -94, -125, -106, -130, -173, -176, -183, -107, -18, -32, -103, -66, -18, -36, -69, -134, -138, -116, -129, -145, -65, 21, -55, -159, -215, -169, -13, -16, -178, -232, -121, -24, -81, -127, -76, -32, -72, -124, -78, -53, -69, -111, -76, -42, -109, -69, -32, -78, -133, -108, -49, -89, -87, -55, -85, -97, -102, -130, -102, -35, -41, -81, -99, -121, -110, -105, -95, -53, -66, -105, -90, -69, -110, -133, -115, -84, -47, -100, -132, -62, -92, -139, -96, -74, -69, -89, -129, -120, -61, -96, -146, -112, -39, -49, -128, -97, -69, -68, -85, -132, -116, -62, -97, -126, -89, -110, -99, -96, -86, -67, -88, -109, -107, -102, -110, -93, -84, -92, -130, -128, -112, -96, -119, -135, -125, -146, -98, -111, -175, -118, -51, -98, -151, -113, -107, -97, -88, -138, -152, -117, -90, -108, -142, -130, -89, -94, -130, -130, -131, -94, -45, -90, -104, -116, -146, -95, -97, -121, -119, -122, -74, -110, -153, -122, -99, -108, -149, -129, -62, -83, -165, -131, -62, -43, -43, -88, -118, -98, -91, -112, -119, -114, -103, -106, -101, -86, -114, -112, -107, -109, -99, -79, -104, -123, -68, -108, -97, -70, -103, -96, -126, -135, -100, -43, -66, -142, -96, -97, -94, -52, -87, -118, -124, -76, -42, -89, -125, -97, -114, -122, -77, -86, -103, -97, -83, -85, -129, -124, -106, -134, -125, -104, -71, -111, -128, -95, -84, -67, -123, -115, -78, -57, -57, -98, -95, -86, -118, -134, -81, -66, -100, -119, -123, -77, -29, -66, -125, -135, -90, -63, -119, -79, -32, -67, -94, -120, -95, -66, -62, -73, -91, -78, -87, -119, -96, -78, -116, -107, -73, -104, -124, -104, -63, -55, -72, -79, -76, -74, -101, -88, -75, -84, -68, -60, ])\n",
    "out_window_c = np.array([\n",
    "\t\t-116, -225, -142, -122, -155, -195, -173, -174, -172, -162, -212, -199, -173, -96, -140, -212, -219, -238, -181, -144, -140, -140, -161, -244, -235, -171, -167, -170, -185, -192, -208, -251, -232, -265, -302, -244, -267, -241, -194, -257, -280, -321, -365, -224, -266, -354, -267, -255, -295, -399, -468, -442, -372, -346, -415, -447, -479, -422, -430, -504, -425, -407, -428, -382, -413, -600, -607, -473, -478, -430, -465, -485, -426, -498, -524, -486, -482, -477, -472, -651, -570, -496, -594, -618, -652, -579, -608, -480, -591, -879, -738, -678, -749, -693, -857, -696, -625, -888, -619, -585, -859, -1046, -1016, -920, -1076, -800, -730, -1038, -1140, -1106, -895, -1013, -1091, -987, -851, -1124, -1380, -897, -520, -714, -1556, -1453, -1047, -1297, -880, -1192, -1798, -1011, -554, -1147, -1282, -1054, -1056, -1467, -2025, -1710, -1398, -483, -421, -1371, -1072, -826, -1022, -521, -818, -1477, -617, -1192, -1589, -569, -1934, -2615, 540, 599, -2283, -2223, -557, 966, -1675, -4639, -1539, 786, -1246, -1666, -402, -1903, -1736, -1555, -3094, -232, 1180, -1918, -4395, -4823, -2029, 850, 472, -1129, -1716, -1700, -2127, -1640, -1253, -1804, 93, -94, -2654, -2004, -2694, -2530, -398, -345, -763, -1518, -2071, -1659, -1892, -4182, -4968, -1501, 1234, -504, -1895, -319, 146, -936, -2216, -2416, -1700, -1364, -1622, -2298, -1856, -1607, -2154, -1360, -485, -835, -1432, -1909, -1623, -1995, -2661, -2714, -2828, -1657, -279, -498, -1605, -1030, -282, -564, -1083, -2106, -2173, -1829, -2037, -2292, -1029, 333, -873, -2525, -3418, -2689, -207, -255, -2839, -3703, -1932, -383, -1295, -2031, -1215, -512, -1152, -1984, -1248, -848, -1104, -1775, -1215, -671, -1741, -1102, -511, -1244, -2120, -1720, -780, -1415, -1382, -873, -1347, -1536, -1613, -2053, -1608, -551, -645, -1271, -1551, -1893, -1717, -1636, -1477, -823, -1022, -1623, -1388, -1062, -1688, -2036, -1756, -1279, -714, -1515, -1994, -934, -1382, -2081, -1433, -1101, -1023, -1315, -1900, -1761, -892, -1399, -2120, -1620, -562, -703, -1830, -1381, -978, -960, -1195, -1847, -1616, -860, -1339, -1731, -1217, -1497, -1340, -1293, -1153, -893, -1167, -1438, -1404, -1331, -1427, -1200, -1078, -1173, -1648, -1613, -1403, -1195, -1472, -1660, -1527, -1772, -1181, -1329, -2081, -1394, -598, -1142, -1746, -1298, -1220, -1098, -988, -1538, -1681, -1284, -980, -1167, -1522, -1382, -938, -983, -1348, -1336, -1335, -949, -451, -893, -1023, -1130, -1410, -909, -919, -1136, -1106, -1123, -674, -993, -1367, -1079, -867, -936, -1277, -1094, -520, -689, -1355, -1064, -498, -341, -338, -683, -905, -743, -682, -829, -870, -823, -735, -747, -703, -591, -773, -750, -707, -711, -637, -502, -651, -760, -414, -649, -575, -409, -593, -545, -704, -744, -543, -230, -347, -736, -490, -487, -465, -253, -417, -556, -575, -347, -188, -393, -542, -414, -478, -503, -312, -342, -403, -373, -313, -315, -469, -443, -372, -461, -422, -345, -231, -354, -401, -292, -253, -198, -357, -327, -217, -156, -153, -257, -245, -217, -292, -325, -193, -154, -228, -267, -270, -166, -61, -137, -254, -269, -176, -121, -223, -146, -58, -119, -164, -205, -160, -109, -101, -117, -143, -121, -133, -179, -142, -114, -167, -152, -103, -144, -170, -141, -85, -73, -95, -104, -99, -96, -131, -113, -96, -108, -87, -77, ])\n",
    "\n",
    "out_swapped_fft = np.array([\n",
    "\t-3645-3645j, 1202+1588j, 220+213j, -158-64j, 118-48j, -54+13j, -50+58j, 29+2j, 80-55j, -136-22j, 36-7j, -3+52j, -1-66j, -48+59j, 56+31j, -49-44j, 17-46j, -32+70j, 91-20j, -134-28j, 99+78j, -76-97j, 33+128j, 5-70j, -69-41j, 65+53j, 6+10j, -65-61j, 55+60j, -8-66j, -41+1j, 25+41j, -35-31j, 26+8j, 6-54j, -38+32j, 64+2j, -85+1j, 130+26j, -142-136j, -20+189j, 184-116j, -175-47j, 91+157j, -33-91j, -23+5j, 33-69j, -35+118j, 42-71j, -67-30j, 37+61j, 42-14j, -59-33j, -10+122j, 104-135j, -204+24j, 148+71j, 2-144j, -88+153j, 159-38j, -185-133j, 97+216j, -35-189j, -47+51j, 74+90j, 49-187j, -223+166j, 186+6j, 36-160j, -156+117j, 90+34j, 49-90j, -94-49j, 17+133j, -38-41j, 55-38j, -23-54j, -76+117j, 190-13j, -135-46j, 17+90j, 46-64j, 36-117j, -240+178j, 313-2j, -153-150j, -89+122j, 144-17j, -41-79j, 36+34j, -99+67j, 76-50j, 3+34j, 6-152j, -75+141j, 94+26j, -110-148j, 185+83j, -177-89j, 74+146j, 61-107j, -101-23j, 9+121j, 148-136j, -181+78j, 51+57j, 92-126j, -131+5j, -102+110j, 243-99j, -96-96j, -153+178j, 277+56j, -110-289j, -142+170j, 106+42j, 160-110j, -274-16j, 221+116j, -108-118j, -21+72j, -75-103j, 212+145j, -35-234j, -203+119j, 128+141j, 158-202j, -263+27j, 133+199j, 36-308j, 6+237j, -144-194j, 184+326j, -44-357j, -92+64j, -45+444j, 274-449j, -388-88j, 162+483j, 243-482j, -375+204j, 250-119j, -46+129j, 107-164j, -285+220j, 364+10j, -265-434j, -62+472j, 77+28j, 254-445j, -463+232j, 401+296j, -193-577j, -5+345j, 112-182j, -179+83j, 165+82j, -18-148j, -201+61j, 235+29j, 13-109j, -188+136j, 174-80j, -92+10j, -10-86j, -93+133j, 256-190j, -266+48j, 194+261j, -76-222j, -73-19j, 11+113j, 145-63j, -269-77j, 117+377j, 235-468j, -364+137j, 139+162j, 213-73j, -170-194j, -123+369j, 344-264j, -316+51j, 88+112j, 150-117j, -184-60j, -18+175j, 165-156j, -157-121j, -3+362j, 295-337j, -417-3j, 266+192j, 101-149j, -327-104j, 200+232j, -66-230j, -42+239j, 104-128j, -125-28j, 116+177j, -25-191j, -100+133j, 93-64j, -45+0j, 86-33j, -32-3j, -35+30j, -5+44j, 110-64j, -110+11j, 96+30j, -189-104j, 195+316j, 23-450j, -254+367j, 323-119j, -216-160j, 101+225j, -36-140j, -23+112j, 100-70j, -87-3j, 30+84j, 4-100j, -11+25j, 1-45j, 20+32j, 9+39j, -65-71j, 33+15j, 52+92j, -25-128j, -127+49j, 200+20j, -103-99j, 78+132j, -23-169j, -22+120j, 81-48j, -55+22j, -38-55j, 40+66j, 74-50j, -82+2j, 64+2j, 7+40j, -24-146j, -61+90j, 13+35j, 66-53j, 15-56j, -43+131j, -62-159j, 226+228j, 1615+1221j, ])\n",
    "\n",
    "out_rfft = np.array([\n",
    "\t-3645+0j, 1408+185j, 221-6j, -110+49j, 36-87j, -19+34j, 8+55j, 19-14j, 9-74j, -79+63j, 17-22j, 31+25j, -40-36j, 13+56j, 47-18j, -46+8j, -20-34j, 23+55j, 35-69j, -82+72j, 93-33j, -95+5j, 104+56j, -45-61j, -61+43j, 63-25j, 17+1j, -65+12j, 35-15j, -3-16j, -17+24j, 15-11j, -35+37j, 35-28j, -44-30j, 20+59j, 26-61j, -57+60j, 109-86j, -165+23j, 130+143j, -16-248j, -122+203j, 186-78j, -118-6j, 40+12j, -52-43j, 49+67j, -3-36j, -22-9j, 2-8j, 29+19j, -31-2j, 47+56j, -31-108j, -63+112j, 108-73j, -85-26j, 40+101j, 33-142j, -111+105j, 173-62j, -195-25j, 21+93j, 169-43j, -209-95j, 82+244j, 72-190j, -115+8j, 55+127j, 32-94j, -103-18j, 35+87j, 78-33j, -143-69j, 110+159j, -11-185j, -93+78j, 91+35j, 73-38j, -136-113j, 36+238j, 89-169j, -122+36j, 69+7j, -6-38j, -25+14j, -60+76j, 110-99j, -116-58j, 104+130j, -35-84j, -12+40j, -60-20j, 87+65j, -74-94j, -22+60j, 126-9j, -104-34j, -8+64j, 78-34j, -85-48j, 49+97j, 13-184j, -133+280j, 221-122j, -220-154j, 94+242j, 44-4j, -9-253j, -162+203j, 187+22j, -127-137j, 31+88j, -11-60j, 121+64j, -184-123j, 103+256j, 98-243j, -200+34j, 126+230j, -16-223j, -42-39j, -33+176j, 96-160j, -73+93j, 3-120j, 16+153j, 66-100j, -132-14j, 78+99j, 64-74j, -108-57j, -9+115j, 101-80j, -46+51j, -2-31j, -49+49j, 90-60j, -121+23j, 74+34j, 56-17j, -84-81j, -35+152j, 121-54j, -85-62j, -22+34j, 97+32j, -60-45j, -36+18j, 32+26j, 3-2j, -56-47j, 55+58j, 10-54j, -57+5j, 33+61j, 35-83j, -87+41j, 82-36j, -30+79j, 24-39j, -41-46j, 14+61j, 6-20j, 23+7j, -28+1j, -2-51j, -36+72j, 93-27j, -58-57j, -67+93j, 158-24j, -136-90j, -15+79j, 102+36j, -40-90j, -73+67j, 107+5j, -33-77j, -64+26j, 86+46j, -37-22j, -27-44j, -8+53j, 34-3j, 3-23j, -53-9j, 51+28j, 16-11j, -48-6j, 23-2j, 0+8j, 4-7j, 12+18j, -26-54j, -17+57j, 23-3j, -33-40j, 21+32j, 23-20j, -55+6j, 32+26j, -8-36j, -22+15j, 34+10j, -2-39j, -31+21j, 20+22j, -14-23j, 10-3j, -6+26j, 5-13j, -45+1j, 45+3j, -21-6j, 18-10j, -17+12j, 3+14j, -7-10j, -8-5j, 8+9j, 1-4j, -8+10j, 17+3j, -10-19j, -4+2j, 8+33j, -4-25j, -2+7j, 0+5j, -6-6j, 12+0j, -18-2j, 11+3j, 3+5j, -7-5j, 1+2j, -2+2j, 0-3j, -1+0j, 1+4j, 0-1j, -2+2j, -3-1j, -2+1j, 1+2j, -2+1j, -2+0j, 0+3j, -1+0j, -2+0j, -2+3j, -1+2j, -1+2j, -1+3j, 2+0j, ])\n",
    "in_ifft = np.array([\n",
    "\t-1823-1823j, 601+794j, 110+108j, -78-32j, 59-23j, -27+7j, -24+30j, 15+1j, 40-28j, -68-11j, 18-2j, -1+27j, 0-34j, -24+30j, 29+16j, -24-22j, 8-23j, -16+35j, 46-10j, -67-14j, 50+40j, -38-48j, 17+63j, 3-34j, -34-20j, 33+28j, 3+5j, -32-31j, 28+30j, -3-33j, -20+0j, 12+21j, -17-14j, 13+4j, 3-27j, -19+17j, 32+2j, -43+2j, 65+14j, -72-68j, -10+95j, 92-58j, -87-23j, 46+79j, -16-45j, -11+3j, 16-33j, -18+59j, 21-35j, -34-15j, 19+31j, 22-6j, -28-16j, -5+62j, 53-67j, -102+14j, 75+36j, 0-71j, -43+78j, 80-17j, -92-65j, 49+109j, -17-94j, -24+26j, 38+47j, 25-92j, -112+84j, 93+4j, 18-79j, -78+60j, 45+18j, 25-44j, -47-23j, 9+68j, -19-20j, 27-17j, -11-26j, -38+60j, 95-5j, -68-23j, 9+47j, 22-31j, 18-58j, -120+90j, 157+0j, -76-74j, -45+63j, 72-7j, -20-39j, 17+19j, -50+36j, 38-23j, 1+18j, 3-74j, -39+73j, 47+15j, -56-72j, 93+43j, -88-44j, 38+75j, 30-52j, -50-10j, 5+62j, 74-67j, -91+41j, 25+30j, 45-61j, -66+3j, -52+57j, 122-47j, -49-46j, -77+90j, 138+28j, -56-143j, -71+87j, 53+23j, 80-53j, -138-6j, 109+60j, -53-57j, -11+38j, -38-50j, 106+75j, -17-115j, -102+62j, 64+72j, 79-99j, -132+15j, 66+100j, 17-152j, 3+120j, -72-95j, 91+165j, -24-176j, -46+34j, -23+224j, 136-223j, -195-42j, 80+243j, 121-239j, -189+104j, 125-58j, -23+66j, 53-79j, -143+111j, 180+6j, -134-215j, -33+238j, 37+16j, 125-221j, -232+119j, 200+150j, -97-286j, -5+175j, 55-89j, -91+43j, 82+43j, -10-71j, -102+32j, 116+16j, 5-53j, -96+70j, 86-38j, -48+7j, -6-42j, -49+68j, 127-93j, -134+26j, 95+132j, -39-109j, -38-8j, 4+58j, 71-30j, -137-36j, 57+190j, 117-233j, -184+70j, 69+82j, 104-35j, -87-95j, -63+186j, 170-130j, -160+27j, 43+58j, 75-57j, -94-28j, -10+90j, 81-76j, -81-59j, -4+183j, 147-166j, -210+1j, 132+98j, 50-73j, -165-50j, 99+117j, -35-113j, -23+122j, 50-62j, -63-13j, 56+90j, -15-92j, -51+68j, 44-29j, -24+2j, 42-14j, -18+0j, -19+16j, -3+24j, 53-30j, -57+7j, 46+17j, -96-51j, 95+159j, 10-222j, -128+185j, 159-58j, -109-79j, 48+114j, -20-68j, -13+58j, 48-33j, -46+0j, 14+43j, 0-48j, -7+14j, -1-21j, 8+17j, 3+21j, -35-35j, 14+10j, 24+48j, -15-62j, -66+26j, 98+11j, -53-48j, 37+68j, -14-83j, -13+61j, 39-22j, -29+12j, -21-25j, 18+34j, 35-23j, -43+2j, 30+4j, 1+22j, -13-72j, -33+47j, 4+19j, 31-24j, 6-26j, -24+67j, -33-78j, 111+116j, 806+613j, ])\n",
    "\n",
    "out_swapped_fft = np.array([\n",
    "\t-13+3j, -10+1j, -10-1j, -11-1j, -9-2j, -11-3j, -8+0j, -8-3j, -10-3j, -8-2j, -8-1j, -8-4j, -10-2j, -8-2j, -8-3j, -9-4j, -9-7j, -11-6j, -10-6j, -8-6j, -10-8j, -13-4j, -10-10j, -10-6j, -11-9j, -16-12j, -14-10j, -15-12j, -17-11j, -15-14j, -16-11j, -14-10j, -14-17j, -22-13j, -16-11j, -17-13j, -15-14j, -18-13j, -16-12j, -17-18j, -19-14j, -20-18j, -22-15j, -21-13j, -19-25j, -23-20j, -25-19j, -28-21j, -22-25j, -21-16j, -30-31j, -33-26j, -35-22j, -24-31j, -38-33j, -29-30j, -36-28j, -28-33j, -45-26j, -18-20j, -51-43j, -34-38j, -29-35j, -58-29j, -17-37j, -40-33j, -34-45j, -62-54j, -43-14j, -13-43j, -34-26j, -32-17j, -26-47j, -20-38j, -49-18j, -58-82j, 18+18j, -71-69j, -17+30j, -52-145j, -49+25j, -39-52j, -11-59j, -55-49j, -97-7j, 38-59j, -137-151j, -63+27j, 16-36j, -54-52j, -66-52j, -39-56j, 3-3j, -83-62j, -83-80j, -12-10j, -24-47j, -64-51j, -58-131j, -155-47j, 39-16j, -59-10j, 5-29j, -70-75j, -53-42j, -50-72j, -58-49j, -67-43j, -14-26j, -44-59j, -50-62j, -83-86j, -89-52j, -8-15j, -50-33j, -9-18j, -34-65j, -68-57j, -64-71j, -33+10j, -27-79j, -107-84j, -5-8j, -88-116j, -59-11j, -41-63j, -39-17j, -36-61j, -39-27j, -34-55j, -38-21j, -55-35j, -17-38j, -67-55j, -24-44j, -44-27j, -42-47j, -50-64j, -50-17j, -20-40j, -48-60j, -54-50j, -46-27j, -33-50j, -43-33j, -53-64j, -54-40j, -22-46j, -62-30j, -43-64j, -44-34j, -32-42j, -59-55j, -28-44j, -66-50j, -17-22j, -57-43j, -31-30j, -38-59j, -50-26j, -42-53j, -38-45j, -42-41j, -37-29j, -37-44j, -44-41j, -44-38j, -33-36j, -51-50j, -44-38j, -46-51j, -47-55j, -37-41j, -65-44j, -19-35j, -54-41j, -38-35j, -31-48j, -52-41j, -31-36j, -47-44j, -28-31j, -42-41j, -41-30j, -14-28j, -32-35j, -43-28j, -30-36j, -35-35j, -20-30j, -43-33j, -26-29j, -39-35j, -16-21j, -42-33j, -16-10j, -11-22j, -29-23j, -22-26j, -28-25j, -22-23j, -22-20j, -25-24j, -22-22j, -20-16j, -19-23j, -13-20j, -18-13j, -19-17j, -21-22j, -17-7j, -11-23j, -15-15j, -14-9j, -13-17j, -19-11j, -6-12j, -18-12j, -14-16j, -9-10j, -13-11j, -11-10j, -15-14j, -12-14j, -12-11j, -8-11j, -12-9j, -7-7j, -11-10j, -7-4j, -5-9j, -8-7j, -9-10j, -6-4j, -6-9j, -9-5j, -2-4j, -8-9j, -6-4j, -7-4j, -3-4j, -4-5j, -4-3j, -3-4j, -4-5j, -4-5j, -4-3j, -5-6j, -3-5j, -5-4j, -3-2j, -4-4j, -3-3j, -5-5j, -5-3j, -4-5j, ])\n",
    "\n",
    "out_irfft = np.array([\n",
    "\t-13, 3, -10, 1, -10, -1, -11, -1, -9, -2, -11, -3, -8, 0, -8, -3, -10, -3, -8, -2, -8, -1, -8, -4, -10, -2, -8, -2, -8, -3, -9, -4, -9, -7, -11, -6, -10, -6, -8, -6, -10, -8, -13, -4, -10, -10, -10, -6, -11, -9, -16, -12, -14, -10, -15, -12, -17, -11, -15, -14, -16, -11, -14, -10, -14, -17, -22, -13, -16, -11, -17, -13, -15, -14, -18, -13, -16, -12, -17, -18, -19, -14, -20, -18, -22, -15, -21, -13, -19, -25, -23, -20, -25, -19, -28, -21, -22, -25, -21, -16, -30, -31, -33, -26, -35, -22, -24, -31, -38, -33, -29, -30, -36, -28, -28, -33, -45, -26, -18, -20, -51, -43, -34, -38, -29, -35, -58, -29, -17, -37, -40, -33, -34, -45, -62, -54, -43, -14, -13, -43, -34, -26, -32, -17, -26, -47, -20, -38, -49, -18, -58, -82, 18, 18, -71, -69, -17, 30, -52, -145, -49, 25, -39, -52, -11, -59, -55, -49, -97, -7, 38, -59, -137, -151, -63, 27, 16, -36, -54, -52, -66, -52, -39, -56, 3, -3, -83, -62, -83, -80, -12, -10, -24, -47, -64, -51, -58, -131, -155, -47, 39, -16, -59, -10, 5, -29, -70, -75, -53, -42, -50, -72, -58, -49, -67, -43, -14, -26, -44, -59, -50, -62, -83, -86, -89, -52, -8, -15, -50, -33, -9, -18, -34, -65, -68, -57, -64, -71, -33, 10, -27, -79, -107, -84, -5, -8, -88, -116, -59, -11, -41, -63, -39, -17, -36, -61, -39, -27, -34, -55, -38, -21, -55, -35, -17, -38, -67, -55, -24, -44, -44, -27, -42, -47, -50, -64, -50, -17, -20, -40, -48, -60, -54, -50, -46, -27, -33, -50, -43, -33, -53, -64, -54, -40, -22, -46, -62, -30, -43, -64, -44, -34, -32, -42, -59, -55, -28, -44, -66, -50, -17, -22, -57, -43, -31, -30, -38, -59, -50, -26, -42, -53, -38, -45, -42, -41, -37, -29, -37, -44, -44, -41, -44, -38, -33, -36, -51, -50, -44, -38, -46, -51, -47, -55, -37, -41, -65, -44, -19, -35, -54, -41, -38, -35, -31, -48, -52, -41, -31, -36, -47, -44, -28, -31, -42, -41, -41, -30, -14, -28, -32, -35, -43, -28, -30, -36, -35, -35, -20, -30, -43, -33, -26, -29, -39, -35, -16, -21, -42, -33, -16, -10, -11, -22, -29, -23, -22, -26, -28, -25, -22, -23, -22, -20, -25, -24, -22, -22, -20, -16, -19, -23, -13, -20, -18, -13, -19, -17, -21, -22, -17, -7, -11, -23, -15, -15, -14, -9, -13, -17, -19, -11, -6, -12, -18, -12, -14, -16, -9, -10, -13, -11, -11, -10, -15, -14, -12, -14, -12, -11, -8, -11, -12, -9, -7, -7, -11, -10, -7, -4, -5, -9, -8, -7, -9, -10, -6, -4, -6, -9, -9, -5, -2, -4, -8, -9, -6, -4, -7, -4, -3, -4, -4, -5, -4, -3, -3, -4, -4, -5, -4, -5, -4, -3, -5, -6, -3, -5, -5, -4, -3, -2, -4, -4, -3, -3, -5, -5, -5, -3, -4, -5, ])\n",
    "out_iwindow_c = np.array([\n",
    "\t\t-163, 37, -125, 12, -125, -13, -136, -13, -110, -25, -132, -36, -94, 0, -92, -35, -113, -34, -88, -22, -86, -11, -83, -41, -100, -20, -78, -19, -75, -28, -81, -36, -79, -60, -93, -50, -81, -48, -62, -46, -75, -59, -94, -28, -69, -68, -66, -39, -70, -56, -98, -72, -82, -58, -85, -67, -92, -59, -79, -72, -80, -54, -68, -48, -66, -78, -99, -58, -69, -47, -71, -54, -61, -56, -70, -50, -60, -45, -62, -65, -67, -49, -68, -61, -73, -49, -67, -41, -59, -77, -69, -60, -73, -55, -80, -59, -61, -68, -56, -42, -78, -80, -84, -65, -86, -54, -58, -74, -89, -76, -66, -68, -80, -62, -61, -71, -95, -55, -38, -41, -103, -86, -67, -74, -56, -67, -110, -55, -32, -68, -73, -60, -61, -80, -108, -93, -74, -24, -22, -72, -56, -43, -52, -28, -42, -74, -32, -59, -76, -28, -88, -123, 26, 26, -104, -100, -25, 42, -74, -204, -69, 34, -54, -72, -15, -80, -74, -66, -129, -10, 49, -77, -177, -193, -80, 34, 20, -45, -68, -65, -81, -64, -48, -68, 3, -4, -99, -74, -98, -94, -14, -12, -28, -54, -73, -59, -66, -148, -175, -53, 43, -18, -66, -12, 5, -32, -77, -82, -58, -46, -54, -78, -63, -53, -72, -46, -15, -28, -47, -62, -53, -65, -87, -90, -93, -54, -9, -16, -52, -34, -10, -19, -35, -67, -70, -58, -65, -73, -34, 10, -28, -80, -108, -85, -6, -8, -88, -116, -59, -11, -41, -63, -39, -17, -36, -61, -39, -27, -34, -55, -38, -21, -55, -35, -17, -38, -67, -56, -25, -45, -45, -28, -43, -48, -51, -65, -51, -18, -21, -41, -50, -62, -56, -52, -48, -28, -35, -52, -45, -35, -56, -67, -57, -42, -24, -49, -66, -32, -46, -69, -48, -37, -35, -46, -65, -61, -31, -49, -73, -56, -19, -25, -64, -49, -35, -34, -44, -68, -58, -31, -49, -62, -45, -54, -50, -49, -45, -35, -45, -54, -54, -51, -55, -48, -42, -46, -65, -64, -57, -49, -60, -67, -62, -73, -50, -55, -88, -60, -26, -49, -75, -58, -54, -50, -45, -69, -76, -60, -46, -54, -71, -67, -43, -48, -65, -64, -65, -48, -23, -46, -52, -58, -72, -47, -51, -62, -61, -61, -36, -54, -77, -60, -48, -54, -73, -66, -31, -41, -82, -65, -32, -21, -23, -46, -61, -49, -48, -57, -62, -56, -50, -53, -51, -47, -59, -58, -54, -54, -50, -41, -49, -60, -35, -54, -49, -36, -53, -49, -61, -64, -51, -21, -34, -72, -48, -48, -46, -30, -44, -58, -66, -39, -22, -44, -67, -45, -54, -62, -36, -41, -54, -46, -47, -44, -67, -63, -55, -66, -57, -53, -40, -55, -62, -47, -38, -38, -61, -57, -41, -24, -30, -55, -50, -45, -59, -66, -41, -28, -42, -65, -66, -38, -16, -31, -63, -73, -50, -34, -60, -35, -27, -36, -37, -47, -38, -30, -30, -40, -41, -52, -42, -54, -44, -33, -56, -68, -35, -58, -59, -47, -36, -24, -49, -49, -37, -37, -62, -63, -63, -38, -50, -63, ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2951c2b670>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.abs(out_rfft) * 2**(-Q_fft-Shift))\n",
    "plt.plot(np.abs(np.fft.rfft(out_window_c*2**(-Q_in-Shift))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2951aead00>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out_irfft * 2**(-Q_ifft-Shift))\n",
    "plt.plot(out_window_c * 2**(-Q_in-Shift))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2951aa1490>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out_iwindow_c*2)\n",
    "plt.plot(input_frame, '--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FLOAT 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_window_c = np.array([\n",
    "\t\t-0.000222, -0.000430, -0.000271, -0.000233, -0.000295, -0.000373, -0.000330, -0.000332, -0.000329, -0.000308, -0.000405, -0.000380, -0.000329, -0.000183, -0.000268, -0.000405, -0.000418, -0.000453, -0.000345, -0.000274, -0.000266, -0.000267, -0.000307, -0.000465, -0.000448, -0.000325, -0.000318, -0.000324, -0.000353, -0.000366, -0.000396, -0.000479, -0.000443, -0.000505, -0.000577, -0.000465, -0.000508, -0.000460, -0.000371, -0.000491, -0.000534, -0.000611, -0.000696, -0.000427, -0.000507, -0.000676, -0.000509, -0.000486, -0.000564, -0.000762, -0.000892, -0.000844, -0.000709, -0.000659, -0.000792, -0.000852, -0.000914, -0.000805, -0.000821, -0.000962, -0.000810, -0.000776, -0.000816, -0.000728, -0.000788, -0.001144, -0.001159, -0.000902, -0.000912, -0.000821, -0.000887, -0.000925, -0.000812, -0.000950, -0.000999, -0.000928, -0.000920, -0.000910, -0.000901, -0.001241, -0.001088, -0.000947, -0.001133, -0.001179, -0.001244, -0.001104, -0.001160, -0.000915, -0.001127, -0.001677, -0.001407, -0.001294, -0.001429, -0.001322, -0.001634, -0.001328, -0.001192, -0.001695, -0.001180, -0.001115, -0.001638, -0.001995, -0.001937, -0.001755, -0.002052, -0.001525, -0.001392, -0.001979, -0.002175, -0.002110, -0.001707, -0.001932, -0.002080, -0.001883, -0.001623, -0.002143, -0.002631, -0.001712, -0.000992, -0.001362, -0.002968, -0.002772, -0.001998, -0.002474, -0.001678, -0.002273, -0.003430, -0.001929, -0.001057, -0.002187, -0.002446, -0.002011, -0.002014, -0.002799, -0.003863, -0.003262, -0.002667, -0.000922, -0.000803, -0.002615, -0.002044, -0.001576, -0.001949, -0.000993, -0.001561, -0.002818, -0.001176, -0.002274, -0.003031, -0.001086, -0.003690, -0.004987, 0.001030, 0.001142, -0.004354, -0.004240, -0.001063, 0.001842, -0.003194, -0.008849, -0.002935, 0.001500, -0.002377, -0.003177, -0.000766, -0.003630, -0.003312, -0.002966, -0.005902, -0.000443, 0.002251, -0.003658, -0.008382, -0.009199, -0.003870, 0.001620, 0.000900, -0.002153, -0.003273, -0.003242, -0.004057, -0.003129, -0.002391, -0.003441, 0.000178, -0.000179, -0.005062, -0.003822, -0.005139, -0.004825, -0.000760, -0.000658, -0.001455, -0.002896, -0.003950, -0.003163, -0.003608, -0.007977, -0.009476, -0.002863, 0.002354, -0.000962, -0.003614, -0.000609, 0.000278, -0.001785, -0.004227, -0.004607, -0.003242, -0.002602, -0.003093, -0.004384, -0.003541, -0.003065, -0.004108, -0.002593, -0.000925, -0.001594, -0.002731, -0.003641, -0.003095, -0.003806, -0.005076, -0.005176, -0.005394, -0.003161, -0.000533, -0.000949, -0.003061, -0.001965, -0.000537, -0.001076, -0.002066, -0.004018, -0.004144, -0.003489, -0.003885, -0.004373, -0.001963, 0.000635, -0.001664, -0.004817, -0.006520, -0.005130, -0.000395, -0.000486, -0.005415, -0.007062, -0.003685, -0.000731, -0.002469, -0.003873, -0.002318, -0.000976, -0.002197, -0.003784, -0.002380, -0.001617, -0.002105, -0.003386, -0.002318, -0.001280, -0.003322, -0.002102, -0.000974, -0.002373, -0.004043, -0.003281, -0.001487, -0.002699, -0.002636, -0.001664, -0.002570, -0.002929, -0.003076, -0.003915, -0.003068, -0.001051, -0.001229, -0.002425, -0.002959, -0.003610, -0.003276, -0.003121, -0.002818, -0.001569, -0.001950, -0.003095, -0.002647, -0.002025, -0.003220, -0.003884, -0.003350, -0.002440, -0.001362, -0.002889, -0.003803, -0.001781, -0.002635, -0.003969, -0.002733, -0.002100, -0.001952, -0.002509, -0.003624, -0.003360, -0.001702, -0.002668, -0.004043, -0.003090, -0.001072, -0.001341, -0.003490, -0.002634, -0.001866, -0.001831, -0.002279, -0.003523, -0.003082, -0.001640, -0.002554, -0.003302, -0.002321, -0.002855, -0.002557, -0.002467, -0.002199, -0.001704, -0.002226, -0.002743, -0.002678, -0.002539, -0.002723, -0.002289, -0.002056, -0.002238, -0.003144, -0.003077, -0.002676, -0.002280, -0.002808, -0.003166, -0.002912, -0.003379, -0.002253, -0.002535, -0.003970, -0.002659, -0.001141, -0.002177, -0.003331, -0.002475, -0.002326, -0.002093, -0.001885, -0.002934, -0.003207, -0.002449, -0.001869, -0.002225, -0.002902, -0.002635, -0.001789, -0.001874, -0.002570, -0.002548, -0.002546, -0.001811, -0.000859, -0.001703, -0.001951, -0.002156, -0.002689, -0.001733, -0.001753, -0.002166, -0.002110, -0.002142, -0.001286, -0.001893, -0.002607, -0.002058, -0.001653, -0.001784, -0.002436, -0.002087, -0.000992, -0.001314, -0.002584, -0.002029, -0.000950, -0.000651, -0.000644, -0.001302, -0.001726, -0.001417, -0.001300, -0.001581, -0.001660, -0.001571, -0.001402, -0.001425, -0.001340, -0.001127, -0.001475, -0.001430, -0.001349, -0.001356, -0.001215, -0.000957, -0.001242, -0.001449, -0.000790, -0.001237, -0.001096, -0.000780, -0.001131, -0.001039, -0.001343, -0.001418, -0.001035, -0.000438, -0.000663, -0.001404, -0.000935, -0.000930, -0.000887, -0.000483, -0.000795, -0.001061, -0.001097, -0.000661, -0.000360, -0.000749, -0.001035, -0.000789, -0.000912, -0.000959, -0.000595, -0.000653, -0.000768, -0.000711, -0.000597, -0.000601, -0.000895, -0.000845, -0.000709, -0.000880, -0.000806, -0.000658, -0.000441, -0.000676, -0.000765, -0.000557, -0.000483, -0.000378, -0.000680, -0.000624, -0.000415, -0.000297, -0.000291, -0.000491, -0.000467, -0.000414, -0.000557, -0.000620, -0.000367, -0.000293, -0.000436, -0.000508, -0.000515, -0.000316, -0.000117, -0.000260, -0.000484, -0.000512, -0.000335, -0.000230, -0.000426, -0.000278, -0.000110, -0.000227, -0.000312, -0.000392, -0.000305, -0.000208, -0.000192, -0.000223, -0.000273, -0.000230, -0.000253, -0.000341, -0.000271, -0.000217, -0.000319, -0.000290, -0.000196, -0.000275, -0.000325, -0.000270, -0.000162, -0.000140, -0.000182, -0.000198, -0.000189, -0.000183, -0.000249, -0.000216, -0.000184, -0.000205, -0.000166, -0.000146, ])\n",
    "\n",
    "out_swapped_fft = np.array([\n",
    "\t-0.444425-0.444471j, 0.147275+0.194642j, 0.027436+0.026680j, -0.018663-0.007176j, 0.014902-0.005467j, -0.006206+0.001986j, -0.005695+0.007473j, 0.003942+0.000640j, 0.010041-0.006324j, -0.016139-0.002189j, 0.004948-0.000271j, -0.000006+0.006766j, 0.000338-0.007721j, -0.005458+0.007491j, 0.007430+0.004324j, -0.005510-0.004976j, 0.002125-0.005343j, -0.003766+0.008789j, 0.011320-0.002141j, -0.016141-0.003256j, 0.012492+0.009640j, -0.009013-0.011697j, 0.004403+0.015917j, 0.000866-0.008477j, -0.008141-0.004794j, 0.008170+0.006828j, 0.001121+0.001526j, -0.007603-0.007261j, 0.006941+0.007631j, -0.000752-0.007954j, -0.004860+0.000304j, 0.003283+0.005407j, -0.004007-0.003549j, 0.003328+0.001073j, 0.000848-0.006385j, -0.004421+0.004222j, 0.007978+0.000512j, -0.010129+0.000289j, 0.016129+0.003253j, -0.017095-0.016406j, -0.002201+0.023343j, 0.022771-0.013964j, -0.021021-0.005531j, 0.011207+0.019485j, -0.003859-0.010800j, -0.002701+0.000881j, 0.004337-0.008159j, -0.004004+0.014596j, 0.005157-0.008426j, -0.007892-0.003491j, 0.004670+0.007666j, 0.005405-0.001529j, -0.006854-0.003917j, -0.000888+0.015013j, 0.013148-0.016200j, -0.024670+0.003036j, 0.018275+0.008998j, 0.000496-0.017387j, -0.010456+0.018874j, 0.019651-0.004264j, -0.022312-0.015949j, 0.012105+0.026449j, -0.003994-0.022785j, -0.005456+0.006374j, 0.009045+0.010898j, 0.005977-0.022802j, -0.027180+0.020229j, 0.022784+0.000787j, 0.004343-0.019581j, -0.019107+0.014334j, 0.011030+0.004020j, 0.006091-0.010908j, -0.011419-0.006003j, 0.002043+0.016339j, -0.004516-0.005033j, 0.006712-0.004695j, -0.002872-0.006649j, -0.009265+0.014356j, 0.023214-0.001586j, -0.016589-0.005575j, 0.002044+0.011030j, 0.005768-0.007906j, 0.004332-0.014265j, -0.029414+0.021730j, 0.038192-0.000389j, -0.018614-0.018334j, -0.010955+0.014862j, 0.017594-0.002117j, -0.004915-0.009562j, 0.004446+0.004193j, -0.012264+0.008250j, 0.009326-0.006108j, 0.000447+0.004165j, 0.000888-0.018458j, -0.009058+0.017200j, 0.011397+0.003055j, -0.013435-0.018011j, 0.022668+0.010100j, -0.021480-0.010890j, 0.008992+0.017854j, 0.007556-0.013092j, -0.012457-0.002827j, 0.001119+0.014884j, 0.017877-0.016606j, -0.022094+0.009592j, 0.006142+0.006962j, 0.011161-0.015412j, -0.016032+0.000657j, -0.012483+0.013445j, 0.029670-0.012158j, -0.011642-0.011745j, -0.018580+0.021792j, 0.033877+0.006946j, -0.013491-0.035224j, -0.017337+0.020858j, 0.012910+0.005003j, 0.019510-0.013328j, -0.033523-0.001945j, 0.026986+0.014258j, -0.013343-0.014427j, -0.002581+0.008835j, -0.009168-0.012474j, 0.025877+0.017666j, -0.004202-0.028545j, -0.024776+0.014482j, 0.015818+0.017275j, 0.019344-0.024627j, -0.032221+0.003440j, 0.016086+0.024290j, 0.004482-0.037519j, 0.000768+0.028871j, -0.017526-0.023666j, 0.022623+0.039857j, -0.005381-0.043627j, -0.011163+0.007781j, -0.005465+0.054251j, 0.033490-0.054796j, -0.047309-0.010889j, 0.019800+0.058987j, 0.029604-0.058806j, -0.045791+0.024924j, 0.030404-0.014495j, -0.005635+0.015764j, 0.012941-0.020116j, -0.034777+0.026882j, 0.044472+0.001186j, -0.032342-0.053007j, -0.007532+0.057640j, 0.009362+0.003377j, 0.031258-0.054299j, -0.056555+0.028431j, 0.048858+0.036162j, -0.023690-0.070438j, -0.000546+0.042063j, 0.013761-0.022143j, -0.021860+0.010200j, 0.020076+0.010002j, -0.002091-0.018067j, -0.024423+0.007475j, 0.028663+0.003642j, 0.001555-0.013429j, -0.022914+0.016655j, 0.021368-0.009692j, -0.011336+0.001344j, -0.001132-0.010418j, -0.011427+0.016277j, 0.031163-0.023158j, -0.032278+0.005834j, 0.023594+0.031951j, -0.009339-0.027116j, -0.008990-0.002247j, 0.001336+0.013779j, 0.017550-0.007664j, -0.032897-0.009433j, 0.014249+0.045984j, 0.028637-0.057123j, -0.044525+0.016771j, 0.016866+0.019820j, 0.026017-0.008976j, -0.020923-0.023657j, -0.015052+0.045162j, 0.041948-0.032284j, -0.038705+0.006202j, 0.010743+0.013694j, 0.018214-0.014400j, -0.022378-0.007299j, -0.002211+0.021404j, 0.020072-0.019028j, -0.019217-0.014759j, -0.000381+0.044195j, 0.036154-0.041251j, -0.050833-0.000389j, 0.032453+0.023402j, 0.012366-0.018220j, -0.039904-0.012728j, 0.024446+0.028350j, -0.008011-0.027999j, -0.005103+0.029315j, 0.012847-0.015579j, -0.015197-0.003415j, 0.014266+0.021502j, -0.003060-0.023334j, -0.012216+0.016300j, 0.011237-0.007733j, -0.005269-0.000183j, 0.010499-0.004021j, -0.003834-0.000370j, -0.004112+0.003638j, -0.000730+0.005403j, 0.013482-0.007827j, -0.013606+0.001315j, 0.011675+0.003687j, -0.023109-0.012650j, 0.023814+0.038557j, 0.002835-0.054919j, -0.030982+0.044839j, 0.039389-0.014412j, -0.026328-0.019518j, 0.012438+0.027516j, -0.004412-0.017029j, -0.002837+0.013698j, 0.012274-0.008605j, -0.010618-0.000405j, 0.003755+0.010224j, 0.000295-0.012240j, -0.001278+0.002999j, 0.000198-0.005554j, 0.002270+0.003855j, 0.001127+0.004883j, -0.007861-0.008704j, 0.004021+0.001858j, 0.006470+0.011166j, -0.003190-0.015620j, -0.015537+0.006138j, 0.024431+0.002345j, -0.012524-0.012096j, 0.009545+0.016231j, -0.002683-0.020701j, -0.002722+0.014627j, 0.009854-0.005622j, -0.006771+0.002796j, -0.004558-0.006744j, 0.005077+0.008122j, 0.008893-0.006168j, -0.010098+0.000398j, 0.007907+0.000308j, 0.000988+0.005039j, -0.002966-0.017820j, -0.007371+0.010886j, 0.001622+0.004201j, 0.007932-0.006469j, 0.001879-0.006965j, -0.005096+0.015896j, -0.007607-0.019441j, 0.027511+0.027778j, 0.197063+0.149044j, ])\n",
    "\n",
    "out_rfft = np.array([\n",
    "\t-0.888896+0.000000j, 0.344305+0.045582j, 0.054695-0.001180j, -0.026231+0.012147j, 0.009621-0.020925j, -0.004401+0.008663j, 0.002120+0.013729j, 0.005094-0.003143j, 0.002752-0.017492j, -0.018770+0.015464j, 0.005092-0.004912j, 0.007988+0.006673j, -0.009268-0.008684j, 0.003511+0.013809j, 0.012184-0.004122j, -0.010708+0.002425j, -0.004440-0.008184j, 0.006003+0.013540j, 0.008852-0.016603j, -0.019511+0.018037j, 0.023209-0.007868j, -0.022720+0.001534j, 0.025888+0.014002j, -0.010743-0.014858j, -0.014714+0.010745j, 0.015640-0.005696j, 0.004633+0.000680j, -0.015323+0.003196j, 0.008946-0.003471j, -0.000636-0.003774j, -0.003871+0.006233j, 0.004058-0.002474j, -0.008327+0.009354j, 0.008818-0.006604j, -0.010312-0.006855j, 0.005411+0.014915j, 0.006681-0.014519j, -0.013539+0.014899j, 0.027195-0.020697j, -0.039782+0.005730j, 0.032335+0.035112j, -0.003541-0.060391j, -0.029512+0.049970j, 0.045758-0.018703j, -0.028488-0.001303j, 0.010202+0.003519j, -0.012326-0.010486j, 0.012344+0.016695j, -0.000679-0.008522j, -0.004872-0.002048j, 0.000953-0.001559j, 0.007195+0.004936j, -0.007236-0.000010j, 0.011743+0.013994j, -0.007297-0.026278j, -0.015037+0.027962j, 0.026787-0.017476j, -0.020361-0.006293j, 0.010054+0.024973j, 0.008459-0.034359j, -0.026641+0.026080j, 0.042550-0.015078j, -0.047190-0.005789j, 0.005439+0.022812j, 0.041152-0.010103j, -0.050856-0.022745j, 0.020174+0.060188j, 0.018023-0.046065j, -0.027696+0.002400j, 0.013603+0.031415j, 0.007700-0.022642j, -0.024896-0.004004j, 0.008379+0.021484j, 0.019172-0.007684j, -0.034727-0.016595j, 0.027156+0.039177j, -0.002383-0.045001j, -0.022543+0.019361j, 0.022720+0.008820j, 0.017961-0.009107j, -0.032878-0.027364j, 0.009056+0.058099j, 0.021965-0.040871j, -0.029413+0.009434j, 0.016949+0.001785j, -0.001167-0.009066j, -0.006009+0.003551j, -0.014718+0.018812j, 0.027188-0.023910j, -0.027906-0.013727j, 0.025493+0.032125j, -0.008146-0.020313j, -0.002392+0.009780j, -0.014344-0.004666j, 0.021584+0.016179j, -0.017643-0.022617j, -0.005032+0.015101j, 0.031000-0.002038j, -0.024939-0.008119j, -0.001781+0.016130j, 0.019189-0.007983j, -0.020405-0.011529j, 0.012304+0.023943j, 0.003728-0.044251j, -0.032487+0.068896j, 0.054018-0.029360j, -0.053593-0.037225j, 0.023642+0.059440j, 0.011078-0.000471j, -0.001774-0.061323j, -0.039184+0.049952j, 0.046168+0.005593j, -0.030818-0.033253j, 0.007653+0.022068j, -0.002590-0.014494j, 0.029539+0.015824j, -0.044587-0.029652j, 0.025229+0.062778j, 0.024310-0.059151j, -0.048601+0.008940j, 0.031151+0.056453j, -0.003680-0.054016j, -0.010177-0.009109j, -0.007593+0.043523j, 0.023927-0.038661j, -0.017633+0.023050j, 0.000823-0.029098j, 0.004271+0.037743j, 0.016086-0.024290j, -0.032011-0.003216j, 0.019289+0.024399j, 0.015924-0.017891j, -0.026081-0.013287j, -0.001990+0.028441j, 0.024891-0.018994j, -0.010953+0.012710j, -0.000241-0.007178j, -0.012052+0.012478j, 0.022476-0.014422j, -0.029148+0.005916j, 0.018306+0.008600j, 0.013775-0.003673j, -0.020381-0.019588j, -0.008203+0.037176j, 0.029917-0.013318j, -0.020276-0.015013j, -0.004800+0.008691j, 0.023912+0.008475j, -0.014199-0.010539j, -0.008416+0.004484j, 0.008198+0.006618j, 0.000981-0.000161j, -0.013297-0.011133j, 0.013603+0.014419j, 0.002577-0.013085j, -0.013912+0.001498j, 0.008443+0.015110j, 0.008682-0.019792j, -0.020964+0.010246j, 0.020331-0.008495j, -0.006849+0.019682j, 0.006127-0.009017j, -0.009274-0.010713j, 0.003896+0.015135j, 0.001708-0.004803j, 0.006044+0.002072j, -0.006594+0.000717j, 0.000074-0.012087j, -0.008509+0.017604j, 0.022973-0.006188j, -0.013937-0.013558j, -0.016111+0.023047j, 0.038793-0.005490j, -0.032898-0.021730j, -0.003383+0.019378j, 0.025349+0.008881j, -0.009603-0.021624j, -0.017684+0.016287j, 0.026511+0.001429j, -0.007646-0.018652j, -0.015542+0.006811j, 0.021505+0.011588j, -0.008493-0.005360j, -0.006386-0.010328j, -0.001584+0.013087j, 0.008610-0.000394j, 0.001119-0.005258j, -0.012638-0.001946j, 0.012822+0.007222j, 0.004380-0.002657j, -0.011200-0.001292j, 0.006000-0.000331j, 0.000346+0.002402j, 0.001472-0.001783j, 0.003292+0.004269j, -0.005998-0.013177j, -0.003682+0.014031j, 0.006089-0.000779j, -0.007663-0.009480j, 0.005659+0.007679j, 0.005754-0.004972j, -0.012693+0.001593j, 0.008230+0.006221j, -0.001394-0.008752j, -0.004887+0.003725j, 0.008709+0.002444j, -0.000116-0.009595j, -0.007132+0.005082j, 0.005106+0.005306j, -0.002865-0.005729j, 0.003057-0.001013j, -0.001228+0.006324j, 0.001521-0.003153j, -0.010736+0.000369j, 0.011326+0.000582j, -0.004671-0.001587j, 0.004853-0.002644j, -0.003641+0.002618j, 0.001372+0.003566j, -0.001002-0.002418j, -0.001540-0.001333j, 0.002442+0.002089j, 0.000541-0.000875j, -0.001735+0.002547j, 0.004615+0.000663j, -0.002053-0.004882j, -0.000792+0.000375j, 0.002154+0.008034j, -0.000878-0.006219j, -0.000142+0.001753j, 0.000510+0.001012j, -0.001000-0.001359j, 0.003384-0.000081j, -0.003928-0.000244j, 0.002946+0.000429j, 0.001183+0.001135j, -0.001171-0.001277j, 0.000687+0.000592j, -0.000255+0.000166j, 0.000084-0.000871j, -0.000207-0.000045j, 0.000640+0.000656j, 0.000323-0.000324j, -0.000075+0.000150j, -0.000493-0.000565j, -0.000087+0.000215j, 0.000844+0.000398j, -0.000335-0.000166j, -0.000082-0.000283j, 0.000470+0.000417j, 0.000117-0.000213j, 0.000073-0.000288j, 0.000186+0.000439j, -0.000039-0.000118j, 0.000252-0.000082j, 0.000034-0.000015j, 0.000326+0.000000j, ])\n",
    "\n",
    "out_irfft = np.array([\n",
    "\t-0.000222, -0.000430, -0.000272, -0.000233, -0.000295, -0.000373, -0.000330, -0.000332, -0.000329, -0.000308, -0.000405, -0.000380, -0.000330, -0.000183, -0.000268, -0.000405, -0.000418, -0.000453, -0.000345, -0.000274, -0.000266, -0.000267, -0.000307, -0.000465, -0.000448, -0.000325, -0.000319, -0.000324, -0.000353, -0.000366, -0.000396, -0.000479, -0.000443, -0.000505, -0.000577, -0.000465, -0.000509, -0.000460, -0.000371, -0.000491, -0.000534, -0.000611, -0.000696, -0.000427, -0.000507, -0.000676, -0.000509, -0.000486, -0.000564, -0.000761, -0.000892, -0.000843, -0.000709, -0.000659, -0.000792, -0.000852, -0.000914, -0.000805, -0.000821, -0.000962, -0.000810, -0.000776, -0.000816, -0.000728, -0.000788, -0.001144, -0.001159, -0.000902, -0.000912, -0.000820, -0.000887, -0.000925, -0.000812, -0.000950, -0.000999, -0.000928, -0.000920, -0.000910, -0.000901, -0.001241, -0.001088, -0.000947, -0.001133, -0.001179, -0.001245, -0.001104, -0.001160, -0.000915, -0.001127, -0.001677, 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    "out_iwindow_c = np.array([\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2951d73220>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.abs(out_rfft))\n",
    "plt.plot(np.abs(out_rfft_np))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2951c70730>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out_iwindow_c)\n",
    "plt.plot(frame, '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FLOAT 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_window_c = np.array([\n",
    "\t\t-0.000222, -0.000431, -0.000271, -0.000232, -0.000296, -0.000374, -0.000328, -0.000332, -0.000328, -0.000309, -0.000404, -0.000380, -0.000330, -0.000183, -0.000267, -0.000404, -0.000418, -0.000452, -0.000345, -0.000273, -0.000267, -0.000267, -0.000307, -0.000463, -0.000448, -0.000326, -0.000319, -0.000324, -0.000353, -0.000366, -0.000395, -0.000479, -0.000443, -0.000504, -0.000576, -0.000465, -0.000507, -0.000462, -0.000370, -0.000492, -0.000534, -0.000614, -0.000694, -0.000427, -0.000507, -0.000675, -0.000507, -0.000484, -0.000565, -0.000763, -0.000893, -0.000847, -0.000710, -0.000656, -0.000793, -0.000854, -0.000912, -0.000805, -0.000820, -0.000965, -0.000809, -0.000774, -0.000816, -0.000729, -0.000786, -0.001144, -0.001160, -0.000904, -0.000912, -0.000824, -0.000889, -0.000923, -0.000813, -0.000954, -0.000999, -0.000927, -0.000923, -0.000912, -0.000900, -0.001244, -0.001091, -0.000942, -0.001137, -0.001175, -0.001244, -0.001099, -0.001160, -0.000912, -0.001129, -0.001671, -0.001404, -0.001297, -0.001427, -0.001320, -0.001640, -0.001328, -0.001190, -0.001701, -0.001175, -0.001114, -0.001640, -0.001984, -0.001938, -0.001755, -0.002060, -0.001518, -0.001389, -0.001984, -0.002182, -0.002121, -0.001701, -0.001930, -0.002075, -0.001884, -0.001625, -0.002151, -0.002640, -0.001709, -0.000992, -0.001358, -0.002960, -0.002762, -0.001999, -0.002472, -0.001678, -0.002274, -0.003433, -0.001930, -0.001060, -0.002182, -0.002441, -0.002014, -0.002014, -0.002808, -0.003860, -0.003265, -0.002670, -0.000923, -0.000801, -0.002609, -0.002045, -0.001572, -0.001953, -0.000992, -0.001564, -0.002823, -0.001175, -0.002274, -0.003021, -0.001091, -0.003693, -0.004974, 0.001030, 0.001144, -0.004364, -0.004242, -0.001060, 0.001846, -0.003189, -0.008850, -0.002945, 0.001503, -0.002365, -0.003189, -0.000767, -0.003632, -0.003326, -0.002975, -0.005890, -0.000443, 0.002258, -0.003662, -0.008362, -0.009155, -0.003876, 0.001625, 0.000900, -0.002151, -0.003265, -0.003250, -0.004059, -0.003128, -0.002396, -0.003433, 0.000177, -0.000178, -0.005066, -0.003830, -0.005157, -0.004822, -0.000759, -0.000660, -0.001457, -0.002899, -0.003967, -0.003159, -0.003616, -0.007996, -0.009460, -0.002869, 0.002365, -0.000965, -0.003601, -0.000610, 0.000278, -0.001785, -0.004242, -0.004608, -0.003235, -0.002594, -0.003098, -0.004395, -0.003540, -0.003067, -0.004120, -0.002594, -0.000923, -0.001595, -0.002731, -0.003632, -0.003098, -0.003815, -0.005066, -0.005188, -0.005402, -0.003159, -0.000534, -0.000950, -0.003052, -0.001968, -0.000538, -0.001076, -0.002060, -0.004028, -0.004150, -0.003479, -0.003891, -0.004364, -0.001953, 0.000637, -0.001663, -0.004822, -0.006500, -0.005127, -0.000395, -0.000486, -0.005402, -0.007050, -0.003677, -0.000732, -0.002472, -0.003876, -0.002319, -0.000977, -0.002197, -0.003784, -0.002380, -0.001617, -0.002106, -0.003387, -0.002319, -0.001282, -0.003326, -0.002090, -0.000973, -0.002365, -0.004028, -0.003281, -0.001488, -0.002701, -0.002640, -0.001663, -0.002579, -0.002930, -0.003082, -0.003906, -0.003067, -0.001053, -0.001228, -0.002426, -0.002960, -0.003601, -0.003281, -0.003113, -0.002823, -0.001564, -0.001953, -0.003098, -0.002655, -0.002029, -0.003220, -0.003891, -0.003342, -0.002441, -0.001358, -0.002884, -0.003815, -0.001778, -0.002625, -0.003967, -0.002731, -0.002106, -0.001953, -0.002502, -0.003632, -0.003357, -0.001701, -0.002670, -0.004028, -0.003082, -0.001076, -0.001343, -0.003494, -0.002640, -0.001869, -0.001831, -0.002274, -0.003525, -0.003082, -0.001640, -0.002548, -0.003311, -0.002319, -0.002853, -0.002563, -0.002472, -0.002197, -0.001701, -0.002228, -0.002747, -0.002686, -0.002548, -0.002731, -0.002289, -0.002060, -0.002243, -0.003143, -0.003082, -0.002670, -0.002274, -0.002808, -0.003174, -0.002899, -0.003372, -0.002258, -0.002533, -0.003967, -0.002655, -0.001144, -0.002167, -0.003326, -0.002472, -0.002319, -0.002090, -0.001892, -0.002930, -0.003204, -0.002457, -0.001869, -0.002228, -0.002899, -0.002640, -0.001793, -0.001869, -0.002579, -0.002548, -0.002548, -0.001816, -0.000858, -0.001709, -0.001945, -0.002151, -0.002686, -0.001732, -0.001755, -0.002167, -0.002121, -0.002136, -0.001289, -0.001892, -0.002609, -0.002045, -0.001656, -0.001793, -0.002426, -0.002090, -0.000992, -0.001312, -0.002579, -0.002029, -0.000946, -0.000652, -0.000645, -0.001297, -0.001724, -0.001419, -0.001305, -0.001579, -0.001663, -0.001572, -0.001396, -0.001419, -0.001343, -0.001129, -0.001472, -0.001427, -0.001343, -0.001358, -0.001213, -0.000957, -0.001236, -0.001450, -0.000790, -0.001236, -0.001099, -0.000782, -0.001129, -0.001038, -0.001343, -0.001419, -0.001038, -0.000439, -0.000660, -0.001404, -0.000931, -0.000931, -0.000885, -0.000484, -0.000793, -0.001060, -0.001091, -0.000660, -0.000360, -0.000748, -0.001038, -0.000793, -0.000912, -0.000961, -0.000595, -0.000652, -0.000767, -0.000710, -0.000599, -0.000599, -0.000896, -0.000847, -0.000710, -0.000877, -0.000805, -0.000656, -0.000441, -0.000675, -0.000767, -0.000557, -0.000483, -0.000378, -0.000683, -0.000626, -0.000414, -0.000298, -0.000292, -0.000492, -0.000467, -0.000416, -0.000557, -0.000618, -0.000366, -0.000294, -0.000435, -0.000507, -0.000515, -0.000317, -0.000117, -0.000259, -0.000484, -0.000511, -0.000336, -0.000230, -0.000425, -0.000278, -0.000110, -0.000227, -0.000313, -0.000391, -0.000305, -0.000209, -0.000193, -0.000223, -0.000273, -0.000230, -0.000254, -0.000340, -0.000271, -0.000217, -0.000319, -0.000290, -0.000196, -0.000277, -0.000324, -0.000269, -0.000161, -0.000140, -0.000182, -0.000197, -0.000189, -0.000183, -0.000250, -0.000216, -0.000183, -0.000205, -0.000166, -0.000147, ])\n",
    "\n",
    "out_swapped_fft = np.array([\n",
    "\t-0.445312-0.445312j, 0.147461+0.195312j, 0.027344+0.026611j, -0.018555-0.007324j, 0.015381-0.005249j, -0.006226+0.001709j, -0.005676+0.007507j, 0.003937+0.000122j, 0.009766-0.005981j, -0.016113-0.002380j, 0.004730-0.000366j, -0.000183+0.006958j, 0.000488-0.007629j, -0.005249+0.007202j, 0.007202+0.004395j, -0.005798-0.005188j, 0.002075-0.005859j, -0.003784+0.008789j, 0.011292-0.002319j, -0.016357-0.003174j, 0.012207+0.009766j, -0.008911-0.011719j, 0.004517+0.016235j, 0.000977-0.008545j, -0.007751-0.004639j, 0.007935+0.007080j, 0.001068+0.001648j, -0.007599-0.007172j, 0.006775+0.007202j, -0.000992-0.007935j, -0.004883+0.000366j, 0.003479+0.005310j, -0.004242-0.003815j, 0.003235+0.001221j, 0.000732-0.006287j, -0.004456+0.004395j, 0.008057+0.000488j, -0.010071+0.000305j, 0.016235+0.003418j, -0.017334-0.016357j, -0.002319+0.023315j, 0.022827-0.013672j, -0.020874-0.005463j, 0.010864+0.019531j, -0.003937-0.010864j, -0.002563+0.001251j, 0.004395-0.008301j, -0.004150+0.014648j, 0.005371-0.008606j, -0.008057-0.003662j, 0.004517+0.007751j, 0.005402-0.001465j, -0.006866-0.004028j, -0.001099+0.014771j, 0.013000-0.016235j, -0.024536+0.002991j, 0.018433+0.009277j, 0.000549-0.017334j, -0.010376+0.018921j, 0.019653-0.004272j, -0.022095-0.015991j, 0.011963+0.026611j, -0.004028-0.022949j, -0.005371+0.006836j, 0.008789+0.011230j, 0.005859-0.022583j, -0.027100+0.020264j, 0.022705+0.000549j, 0.004089-0.019287j, -0.019043+0.014526j, 0.011230+0.003906j, 0.006104-0.010864j, -0.011047-0.005737j, 0.001877+0.016235j, -0.004456-0.005066j, 0.006836-0.004822j, -0.003235-0.007019j, -0.009155+0.014526j, 0.023438-0.001312j, -0.016602-0.005341j, 0.002136+0.011597j, 0.005524-0.008057j, 0.004150-0.014282j, -0.029053+0.021851j, 0.038086-0.000397j, -0.018188-0.018311j, -0.010986+0.014771j, 0.017456-0.001587j, -0.005005-0.010010j, 0.004333+0.004089j, -0.012085+0.008423j, 0.009277-0.005951j, 0.000404+0.004211j, 0.001038-0.018066j, -0.009155+0.017334j, 0.011230+0.002441j, -0.012939-0.017944j, 0.022705+0.009949j, -0.021484-0.010864j, 0.009033+0.017822j, 0.007507-0.012939j, -0.012695-0.003067j, 0.001129+0.014465j, 0.017944-0.016113j, -0.021973+0.009521j, 0.006256+0.006805j, 0.011230-0.015198j, -0.015869+0.000488j, -0.012573+0.013245j, 0.029541-0.012085j, -0.011963-0.011597j, -0.018677+0.021484j, 0.033691+0.006775j, -0.013672-0.035156j, -0.017212+0.020508j, 0.012817+0.004791j, 0.019287-0.012695j, -0.033203-0.002167j, 0.026855+0.014465j, -0.013550-0.014099j, -0.002502+0.008545j, -0.008972-0.012390j, 0.025757+0.017578j, -0.004150-0.028442j, -0.024902+0.014343j, 0.015747+0.017212j, 0.019409-0.024658j, -0.031738+0.003906j, 0.016602+0.023438j, 0.004883-0.037598j, 0.000305+0.028564j, -0.017334-0.023682j, 0.022705+0.039551j, -0.005554-0.043701j, -0.011169+0.007751j, -0.005402+0.054199j, 0.033203-0.054199j, -0.047363-0.010681j, 0.019775+0.059082j, 0.029297-0.058594j, -0.045898+0.025391j, 0.030151-0.014282j, -0.005615+0.015869j, 0.013000-0.020264j, -0.034180+0.026611j, 0.044434+0.001404j, -0.032227-0.053223j, -0.007568+0.057861j, 0.009521+0.003632j, 0.031128-0.054688j, -0.056641+0.028442j, 0.048584+0.035889j, -0.023193-0.070312j, -0.000397+0.042236j, 0.013672-0.021973j, -0.021729+0.010132j, 0.020264+0.009888j, -0.002243-0.018188j, -0.024170+0.007690j, 0.028809+0.003342j, 0.001312-0.012817j, -0.022705+0.016602j, 0.021240-0.009705j, -0.011414+0.001404j, -0.001312-0.010498j, -0.010925+0.016357j, 0.031128-0.023193j, -0.032471+0.005920j, 0.023560+0.031982j, -0.009399-0.027100j, -0.009155-0.002106j, 0.001434+0.013672j, 0.017334-0.007568j, -0.033203-0.009399j, 0.014038+0.045898j, 0.029053-0.057129j, -0.044922+0.017090j, 0.017090+0.020020j, 0.026001-0.008972j, -0.020996-0.023682j, -0.015137+0.044922j, 0.041992-0.032227j, -0.038818+0.006256j, 0.010620+0.013855j, 0.018188-0.014038j, -0.022461-0.007568j, -0.002106+0.021606j, 0.020386-0.018921j, -0.019165-0.014771j, -0.000061+0.044189j, 0.036133-0.041504j, -0.050537-0.000977j, 0.032227+0.022949j, 0.012207-0.018677j, -0.039795-0.012939j, 0.024414+0.028320j, -0.008240-0.027832j, -0.005066+0.029419j, 0.012939-0.015503j, -0.015625-0.003204j, 0.014343+0.021606j, -0.003098-0.023315j, -0.012024+0.016357j, 0.010986-0.008118j, -0.005188-0.000320j, 0.010376-0.004028j, -0.003601+0.000031j, -0.004211+0.003937j, -0.001038+0.005280j, 0.013794-0.007477j, -0.013428+0.001312j, 0.011475+0.003540j, -0.023193-0.012817j, 0.023804+0.038330j, 0.002960-0.055176j, -0.030884+0.044434j, 0.039551-0.014526j, -0.026367-0.019287j, 0.012451+0.027222j, -0.004486-0.016724j, -0.002838+0.013855j, 0.012329-0.008362j, -0.010864-0.000488j, 0.003906+0.010376j, 0.000214-0.012329j, -0.001221+0.002991j, 0.000061-0.005371j, 0.002228+0.003845j, 0.001160+0.004639j, -0.008057-0.008789j, 0.003754+0.001892j, 0.006592+0.011047j, -0.003418-0.015381j, -0.015259+0.006195j, 0.024170+0.002136j, -0.012512-0.012085j, 0.009766+0.016113j, -0.002686-0.020874j, -0.002808+0.014648j, 0.009766-0.005432j, -0.006958+0.002869j, -0.004639-0.006592j, 0.005157+0.007996j, 0.009155-0.006012j, -0.010010+0.000305j, 0.007690+0.000275j, 0.001038+0.005066j, -0.002808-0.017578j, -0.007263+0.010620j, 0.001663+0.004333j, 0.007690-0.006348j, 0.001709-0.006714j, -0.005127+0.015869j, -0.007721-0.019653j, 0.027466+0.027832j, 0.197266+0.148438j, ])\n",
    "\n",
    "out_rfft = np.array([\n",
    "\t-0.890625+0.000000j, 0.343750+0.046387j, 0.054688-0.001221j, -0.026367+0.012085j, 0.009949-0.020996j, -0.004517+0.008301j, 0.002075+0.013550j, 0.004944-0.003418j, 0.002716-0.017090j, -0.018677+0.015259j, 0.005005-0.004822j, 0.007874+0.006775j, -0.009155-0.008606j, 0.003693+0.013611j, 0.012085-0.003876j, -0.010925+0.002350j, -0.004791-0.008484j, 0.006042+0.013428j, 0.008667-0.016724j, -0.019653+0.018311j, 0.023193-0.007507j, -0.022705+0.001457j, 0.025879+0.014038j, -0.010559-0.014893j, -0.014526+0.010376j, 0.015625-0.005371j, 0.004517+0.000595j, -0.015442+0.003189j, 0.008606-0.003357j, -0.000740-0.003662j, -0.003860+0.006073j, 0.004120-0.002563j, -0.008545+0.009399j, 0.009033-0.006561j, -0.010498-0.006836j, 0.005646+0.014893j, 0.006775-0.014648j, -0.013428+0.014648j, 0.027222-0.020508j, -0.039551+0.005707j, 0.032227+0.035400j, -0.003540-0.060059j, -0.029541+0.050293j, 0.045654-0.018433j, -0.028687-0.001221j, 0.010193+0.003586j, -0.012207-0.010498j, 0.012756+0.016602j, -0.000992-0.008728j, -0.004944-0.002289j, 0.001297-0.001694j, 0.007111+0.004913j, -0.007294+0.000114j, 0.011230+0.014282j, -0.007172-0.026367j, -0.015015+0.027954j, 0.027100-0.017578j, -0.020630-0.006622j, 0.010193+0.024902j, 0.008545-0.034424j, -0.026855+0.025879j, 0.042480-0.014954j, -0.047119-0.005554j, 0.005310+0.023193j, 0.041016-0.009644j, -0.050781-0.022217j, 0.020142+0.060547j, 0.018188-0.045898j, -0.027588+0.002655j, 0.014038+0.031494j, 0.007812-0.022827j, -0.025024-0.003815j, 0.008667+0.021118j, 0.019043-0.007782j, -0.034912-0.016846j, 0.027222+0.039062j, -0.002716-0.044922j, -0.022583+0.019287j, 0.022705+0.008728j, 0.018311-0.009216j, -0.033203-0.027832j, 0.009277+0.058594j, 0.021729-0.040771j, -0.029663+0.009216j, 0.016724+0.001648j, -0.001038-0.009033j, -0.006134+0.003357j, -0.014648+0.018799j, 0.027100-0.023926j, -0.028076-0.013855j, 0.025513+0.032227j, -0.007660-0.020264j, -0.002579+0.009827j, -0.014343-0.004791j, 0.021484+0.016235j, -0.017578-0.022461j, 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0.029663-0.013062j, -0.020264-0.014709j, -0.005127+0.008789j, 0.023682+0.008301j, -0.014221-0.010315j, -0.008240+0.004639j, 0.008301+0.006470j, 0.001221-0.000122j, -0.013123-0.011108j, 0.013550+0.013916j, 0.002594-0.012634j, -0.014099+0.001770j, 0.008423+0.015015j, 0.008728-0.019897j, -0.020996+0.010254j, 0.020508-0.008362j, -0.006500+0.019409j, 0.006104-0.008301j, -0.009399-0.010864j, 0.003967+0.014771j, 0.001663-0.004883j, 0.006012+0.002014j, -0.006470+0.000488j, 0.000000-0.012024j, -0.008545+0.018066j, 0.022705-0.006653j, -0.014038-0.013489j, -0.015625+0.022949j, 0.038574-0.005524j, -0.032715-0.021973j, -0.003601+0.019287j, 0.025391+0.009399j, -0.009644-0.022217j, -0.017822+0.016113j, 0.026611+0.001083j, -0.007568-0.018799j, -0.015625+0.007202j, 0.021606+0.011719j, -0.008362-0.005432j, -0.006561-0.010132j, -0.001526+0.012817j, 0.008667-0.000519j, 0.001312-0.005127j, -0.012695-0.001953j, 0.012451+0.007172j, 0.004517-0.002197j, -0.011108-0.001343j, 0.005981-0.000549j, 0.000122+0.002075j, 0.001526-0.002319j, 0.003174+0.004456j, -0.006226-0.013245j, -0.003418+0.013977j, 0.006073-0.000732j, -0.007629-0.009460j, 0.005524+0.007538j, 0.005676-0.005188j, -0.012573+0.001587j, 0.008179+0.006409j, -0.001373-0.008667j, -0.004791+0.003815j, 0.008667+0.002350j, -0.000381-0.009399j, -0.007324+0.005310j, 0.005310+0.005219j, -0.003113-0.005493j, 0.003174-0.000824j, -0.001282+0.005859j, 0.001587-0.003174j, -0.010986+0.000366j, 0.011536+0.000366j, -0.004517-0.001831j, 0.004761-0.002441j, -0.004028+0.002777j, 0.001465+0.003296j, -0.001129-0.002441j, -0.001541-0.001312j, 0.002228+0.002106j, 0.000366-0.001038j, -0.001862+0.002594j, 0.004547+0.000854j, -0.001862-0.004883j, -0.000961+0.000336j, 0.001984+0.008118j, -0.000671-0.005920j, -0.000183+0.001572j, 0.000305+0.000839j, -0.001160-0.001389j, 0.003387-0.000366j, -0.003738-0.000092j, 0.002869-0.000031j, 0.001221+0.001091j, -0.001221-0.001160j, 0.000610+0.000549j, -0.000214+0.000244j, -0.000061-0.000824j, -0.000092+0.000244j, 0.000488+0.000946j, 0.000244-0.000275j, 0.000221+0.000427j, -0.000366-0.000671j, -0.000366+0.000076j, 0.000778+0.000595j, -0.000244+0.000092j, -0.000214-0.000427j, 0.000687+0.000786j, -0.000061-0.000305j, 0.000000-0.000092j, 0.000305+0.000061j, 0.000183-0.000244j, 0.000122+0.000004j, 0.000000-0.000610j, 0.000000+0.000000j, ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2951895160>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out_window_c)\n",
    "plt.plot(win_frame, '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "in_ifft = np.array([\n",
    "\t-0.445312-0.445312j, 0.146484+0.195312j, 0.027344+0.026733j, -0.018555-0.007385j, 0.015381-0.005157j, -0.006195+0.001709j, -0.005676+0.007507j, 0.003937+0.000122j, 0.009888-0.005981j, -0.016113-0.002441j, 0.004730-0.000366j, -0.000183+0.006989j, 0.000458-0.007629j, -0.005249+0.007202j, 0.007202+0.004395j, -0.005798-0.005219j, 0.002045-0.005859j, -0.003784+0.008789j, 0.011292-0.002319j, -0.016357-0.003113j, 0.012207+0.009705j, -0.008911-0.011719j, 0.004578+0.016113j, 0.001038-0.008545j, -0.007751-0.004700j, 0.007935+0.007111j, 0.001068+0.001648j, -0.007568-0.007202j, 0.006775+0.007202j, -0.000992-0.007935j, -0.004883+0.000366j, 0.003479+0.005310j, -0.004211+0.003723j, 0.003265-0.001190j, 0.000732+0.006287j, -0.004486-0.004456j, 0.008057-0.000488j, -0.010010-0.000366j, 0.016235-0.003479j, -0.017334+0.016235j, -0.002441-0.023315j, 0.022827+0.013672j, -0.020996+0.005371j, 0.010864-0.019531j, -0.003845+0.010864j, -0.002563-0.001274j, 0.004395+0.008301j, -0.004211-0.014648j, 0.005371+0.008606j, -0.008057+0.003662j, 0.004517-0.007721j, 0.005371+0.001465j, -0.006897+0.004028j, -0.001099-0.014771j, 0.013123+0.016235j, -0.024536-0.002991j, 0.018311-0.009277j, 0.000488+0.017334j, -0.010376-0.018921j, 0.019775+0.004272j, -0.021973+0.015991j, 0.011841-0.026733j, -0.004272+0.022827j, -0.005371-0.006805j, 0.008667-0.011353j, 0.005737+0.022461j, -0.026978-0.020386j, 0.022705-0.000488j, 0.003967+0.019287j, -0.019043-0.014465j, 0.011230-0.003906j, 0.006104+0.010864j, -0.011108+0.005737j, 0.001831-0.016235j, -0.004272+0.005157j, 0.006836+0.004883j, -0.003204+0.007080j, -0.009155-0.014465j, 0.023438+0.001312j, -0.016602+0.005280j, 0.002197-0.011536j, 0.005554+0.008057j, 0.004028+0.014160j, -0.029175-0.021851j, 0.038086+0.000381j, -0.018066+0.018311j, -0.011047-0.014771j, 0.017456+0.001648j, -0.005066+0.010010j, 0.004395-0.004089j, -0.012085-0.008423j, 0.009277+0.005859j, 0.000397-0.004211j, 0.001099+0.018066j, -0.009155-0.017334j, 0.011230-0.002319j, -0.012878+0.017822j, 0.022705-0.009949j, -0.021484+0.010864j, 0.009033-0.017944j, 0.007538+0.012939j, -0.012695+0.003067j, 0.001068-0.014465j, 0.017944+0.016113j, -0.021973-0.009766j, 0.006226-0.006958j, 0.011230+0.015198j, -0.015991-0.000366j, -0.012512-0.013245j, 0.029541+0.011963j, -0.011963+0.011719j, -0.018555-0.021484j, 0.033447-0.006775j, -0.013733+0.035156j, -0.017212-0.020508j, 0.012817-0.004730j, 0.019287+0.012634j, -0.033203+0.002441j, 0.026855-0.014404j, -0.013672+0.014038j, -0.002319-0.008423j, -0.008972+0.012451j, 0.025513-0.017578j, -0.004150+0.028809j, -0.025024-0.014404j, 0.015747-0.017212j, 0.019287+0.024658j, -0.031738-0.003906j, 0.016602-0.023438j, 0.004883+0.037598j, 0.000427-0.028564j, -0.017456+0.023804j, 0.022827-0.039551j, -0.005554+0.043945j, -0.010864-0.007690j, -0.005432-0.053955j, 0.033203+0.054199j, -0.047363+0.010620j, 0.019775-0.059326j, 0.029297+0.058594j, -0.045654-0.025391j, 0.030151+0.014282j, -0.005707-0.015869j, 0.013000+0.020264j, -0.033936-0.026367j, 0.044434-0.001465j, -0.032227+0.053223j, -0.007568-0.057861j, 0.009460-0.003601j, 0.031128+0.054688j, -0.056641-0.028320j, 0.048828-0.036133j, -0.023193+0.070312j, -0.000366-0.042236j, 0.013672+0.021973j, -0.021729-0.010132j, 0.020264-0.009888j, -0.002258+0.018188j, -0.024170-0.007690j, 0.028809-0.003357j, 0.001282+0.012695j, -0.022705-0.016479j, 0.021240+0.009705j, -0.011475-0.001404j, -0.001312+0.010498j, -0.010986-0.016357j, 0.031128+0.023315j, -0.032471-0.005920j, 0.023682-0.031982j, -0.009399+0.027100j, -0.009094+0.002045j, 0.001465-0.013672j, 0.017212+0.007568j, -0.033203+0.009399j, 0.014038-0.045898j, 0.029053+0.057373j, -0.045166-0.017090j, 0.017090-0.020142j, 0.025879+0.008911j, -0.020996+0.023682j, -0.015137-0.045166j, 0.041992+0.032227j, -0.039062-0.006256j, 0.010620-0.013855j, 0.018188+0.014038j, -0.022461+0.007568j, -0.002075-0.021606j, 0.020508+0.019043j, -0.019043+0.014832j, 0.000000-0.044189j, 0.036133+0.041504j, -0.050781+0.000793j, 0.032227-0.023071j, 0.012207+0.018677j, -0.039551+0.012817j, 0.024292-0.028442j, -0.008240+0.027954j, -0.005066-0.029419j, 0.012939+0.015503j, -0.015625+0.003235j, 0.014404-0.021729j, -0.003052+0.023438j, -0.012146-0.016479j, 0.010986+0.008179j, -0.005188+0.000320j, 0.010376+0.004028j, -0.003601-0.000031j, -0.004211-0.003937j, -0.001038-0.005310j, 0.013855+0.007446j, -0.013428-0.001358j, 0.011475-0.003540j, -0.023193+0.012817j, 0.023804-0.038330j, 0.002991+0.055176j, -0.030884-0.044434j, 0.039551+0.014526j, -0.026123+0.019165j, 0.012451-0.027344j, -0.004486+0.016724j, -0.002808-0.013794j, 0.012329+0.008362j, -0.010864+0.000473j, 0.003906-0.010376j, 0.000229+0.012268j, -0.001221-0.002991j, 0.000061+0.005371j, 0.002243-0.003845j, 0.001160-0.004639j, -0.008057+0.008789j, 0.003754-0.001892j, 0.006531-0.011108j, -0.003372+0.015442j, -0.015320-0.006195j, 0.024170-0.002136j, -0.012573+0.012085j, 0.009766-0.016113j, -0.002716+0.020996j, -0.002869-0.014648j, 0.009766+0.005463j, -0.006927-0.002869j, -0.004639+0.006622j, 0.005127-0.007996j, 0.009155+0.005981j, -0.010010-0.000305j, 0.007690-0.000275j, 0.001068-0.005066j, -0.002838+0.017578j, -0.007355-0.010620j, 0.001686-0.004333j, 0.007690+0.006348j, 0.001678+0.006714j, -0.005127-0.015869j, -0.007629+0.019775j, 0.027344-0.027954j, 0.197266-0.148438j, ])\n",
    "\n",
    "out_swapped_fft = np.array([\n",
    "\t-0.000244-0.000423j, -0.000290-0.000244j, -0.000320-0.000385j, -0.000336-0.000340j, -0.000347-0.000305j, -0.000397-0.000412j, -0.000324-0.000168j, -0.000252-0.000404j, -0.000420-0.000465j, -0.000366-0.000275j, -0.000282-0.000265j, -0.000290-0.000462j, -0.000443-0.000336j, -0.000351-0.000313j, -0.000359-0.000359j, -0.000412-0.000473j, -0.000465-0.000519j, -0.000603-0.000458j, -0.000488-0.000481j, -0.000380-0.000511j, -0.000572-0.000595j, -0.000740-0.000458j, -0.000473-0.000633j, -0.000504-0.000496j, -0.000580-0.000763j, -0.000919-0.000835j, -0.000755-0.000671j, -0.000797-0.000866j, -0.000931-0.000809j, -0.000858-0.000977j, -0.000820-0.000778j, -0.000839-0.000751j, -0.000801-0.001152j, -0.001160-0.000912j, -0.000908-0.000862j, -0.000893-0.000916j, -0.000805-0.000942j, -0.001030-0.000938j, -0.000916-0.000969j, -0.000912-0.001236j, -0.001099-0.000954j, -0.001144-0.001190j, -0.001266-0.001129j, -0.001160-0.000931j, -0.001129-0.001678j, -0.001434-0.001282j, -0.001419-0.001343j, -0.001640-0.001335j, -0.001205-0.001717j, -0.001190-0.001099j, -0.001648-0.001999j, -0.001945-0.001755j, -0.002075-0.001518j, -0.001419-0.001984j, -0.002151-0.002151j, -0.001694-0.001953j, -0.002060-0.001892j, -0.001610-0.002151j, -0.002640-0.001732j, -0.000984-0.001350j, -0.002991-0.002777j, -0.001999-0.002472j, -0.001678-0.002289j, -0.003433-0.001923j, -0.001068-0.002197j, -0.002441-0.002014j, -0.001999-0.002808j, -0.003860-0.003265j, -0.002686-0.000938j, -0.000828-0.002640j, -0.002060-0.001572j, -0.001953-0.000992j, -0.001572-0.002838j, -0.001175-0.002289j, -0.003036-0.001099j, -0.003677-0.004974j, 0.001007+0.001106j, -0.004395-0.004211j, -0.001060+0.001831j, -0.003174-0.008911j, -0.002991+0.001472j, -0.002365-0.003174j, -0.000778-0.003662j, -0.003357-0.002975j, -0.005920-0.000469j, 0.002228-0.003662j, -0.008362-0.009155j, -0.003876+0.001633j, 0.000916-0.002151j, -0.003265-0.003250j, -0.004089-0.003113j, -0.002396-0.003433j, 0.000160-0.000198j, -0.005066-0.003830j, -0.005188-0.004822j, -0.000790-0.000675j, -0.001465-0.002914j, -0.003967-0.003143j, -0.003677-0.008057j, -0.009399-0.002838j, 0.002350-0.000992j, -0.003601-0.000629j, 0.000265-0.001801j, -0.004211-0.004578j, -0.003265-0.002609j, -0.003082-0.004395j, -0.003571-0.003098j, -0.004089-0.002579j, -0.000938-0.001633j, -0.002747-0.003601j, -0.003113-0.003815j, -0.005066-0.005188j, -0.005402-0.003174j, -0.000530-0.000935j, -0.003052-0.002014j, -0.000538-0.001060j, -0.002075-0.004028j, -0.004150-0.003479j, -0.003891-0.004333j, -0.001938+0.000648j, -0.001663-0.004822j, -0.006470-0.005127j, -0.000420-0.000504j, -0.005371-0.007019j, -0.003693-0.000740j, -0.002472-0.003830j, -0.002319-0.001007j, -0.002197-0.003799j, -0.002411-0.001617j, -0.002090-0.003372j, -0.002335-0.001282j, -0.003326-0.002075j, -0.000977-0.002350j, -0.004028-0.003281j, -0.001526-0.002731j, -0.002625-0.001656j, -0.002594-0.002899j, -0.003082-0.003906j, -0.003082-0.001068j, -0.001205-0.002411j, -0.002975-0.003632j, -0.003250-0.003082j, -0.002838-0.001587j, -0.001938-0.003098j, -0.002686-0.002029j, -0.003204-0.003876j, -0.003342-0.002441j, -0.001343-0.002869j, -0.003784-0.001785j, -0.002594-0.003967j, -0.002731-0.002075j, -0.001968-0.002502j, -0.003632-0.003326j, -0.001709-0.002655j, -0.004028-0.003098j, -0.001083-0.001343j, -0.003494-0.002655j, -0.001846-0.001801j, -0.002258-0.003555j, -0.003067-0.001648j, -0.002563-0.003296j, -0.002319-0.002838j, -0.002563-0.002502j, -0.002167-0.001678j, -0.002228-0.002716j, -0.002655-0.002563j, -0.002762-0.002319j, -0.002029-0.002213j, -0.003143-0.003082j, -0.002655-0.002274j, -0.002823-0.003204j, -0.002899-0.003342j, -0.002274-0.002533j, -0.003937-0.002655j, -0.001114-0.002167j, -0.003296-0.002502j, -0.002319-0.002075j, -0.001892-0.002930j, -0.003204-0.002457j, -0.001854-0.002213j, -0.002869-0.002625j, -0.001755-0.001892j, -0.002579-0.002518j, -0.002548-0.001823j, -0.000866-0.001678j, -0.001945-0.002121j, -0.002670-0.001778j, -0.001732-0.002167j, -0.002136-0.002136j, -0.001297-0.001869j, -0.002594-0.002075j, -0.001633-0.001785j, -0.002441-0.002106j, -0.000984-0.001320j, -0.002579-0.002014j, -0.000961-0.000641j, -0.000633-0.001289j, -0.001740-0.001419j, -0.001312-0.001572j, -0.001625-0.001556j, -0.001404-0.001411j, -0.001328-0.001114j, -0.001480-0.001434j, -0.001358-0.001373j, -0.001221-0.000938j, -0.001236-0.001419j, -0.000801-0.001236j, -0.001099-0.000763j, -0.001137-0.001045j, -0.001358-0.001396j, -0.001038-0.000435j, -0.000671-0.001389j, -0.000946-0.000927j, -0.000877-0.000519j, -0.000793-0.001007j, -0.001106-0.000660j, -0.000351-0.000748j, -0.001038-0.000778j, -0.000954-0.000969j, -0.000576-0.000687j, -0.000771-0.000732j, -0.000626-0.000595j, -0.000885-0.000854j, -0.000721-0.000866j, -0.000793-0.000641j, -0.000443-0.000679j, -0.000778-0.000549j, -0.000519-0.000397j, -0.000710-0.000599j, -0.000420-0.000324j, -0.000280-0.000473j, -0.000473-0.000443j, -0.000557-0.000626j, -0.000359-0.000305j, -0.000458-0.000504j, -0.000519-0.000290j, -0.000114-0.000244j, -0.000488-0.000519j, -0.000336-0.000244j, -0.000427-0.000305j, -0.000153-0.000237j, -0.000324-0.000393j, -0.000290-0.000233j, -0.000179-0.000221j, -0.000290-0.000229j, -0.000290-0.000366j, -0.000290-0.000229j, -0.000347-0.000277j, -0.000195-0.000275j, -0.000351-0.000259j, -0.000175-0.000135j, -0.000214-0.000183j, -0.000214-0.000208j, -0.000267-0.000198j, -0.000175-0.000217j, -0.000175-0.000137j, ])\n",
    "\n",
    "out_irfft = np.array([\n",
    "\t-0.000244, -0.000423, -0.000290, -0.000244, -0.000320, -0.000385, -0.000336, -0.000340, -0.000347, -0.000305, -0.000397, -0.000412, -0.000324, -0.000168, -0.000252, -0.000404, -0.000420, -0.000465, -0.000366, -0.000275, -0.000282, -0.000265, -0.000290, -0.000462, -0.000443, -0.000336, -0.000351, -0.000313, -0.000359, -0.000359, -0.000412, -0.000473, -0.000465, -0.000519, -0.000603, -0.000458, -0.000488, -0.000481, -0.000380, -0.000511, -0.000572, -0.000595, -0.000740, -0.000458, -0.000473, -0.000633, -0.000504, -0.000496, -0.000580, -0.000763, -0.000919, -0.000835, -0.000755, -0.000671, -0.000797, -0.000866, -0.000931, -0.000809, -0.000858, -0.000977, -0.000820, -0.000778, -0.000839, -0.000751, -0.000801, -0.001152, -0.001160, -0.000912, -0.000908, -0.000862, -0.000893, -0.000916, -0.000805, -0.000942, -0.001030, -0.000938, -0.000916, -0.000969, -0.000912, -0.001236, -0.001099, -0.000954, -0.001144, -0.001190, -0.001266, -0.001129, -0.001160, -0.000931, -0.001129, -0.001678, 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    "out_iwindow_c = np.array([\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f295186c550>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out_irfft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
